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dc.contributor.authorKateri, M.en
dc.contributor.authorPapaioannou, T.en
dc.contributor.authorDellaportas, P.en
dc.date.accessioned2015-11-24T17:22:13Z-
dc.date.available2015-11-24T17:22:13Z-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/12579-
dc.rightsDefault Licence-
dc.subjectBayes factor, empirical bayes, Gibbs variable selection, hierarchicalen
dc.subjectlogistic regression, highest posterior density region, matched pairs, Markov chain Monteen
dc.subjectCarlo.en
dc.titleBayesian analysis of correlated proportionsen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μαθηματικώνel
heal.publicationDate2001-
heal.abstractIn this paper we present a Bayesian analysis of 2£2 contingency tables, corresponding to matched pairs designs. We provide Bayes and empirical Bayes estimates for the cell probabilities of these tables as well as the Bayes factor for testing the equality of correlated proportions. The approximate highest posterior density (HPD) region for the difference of the correlated proportions is also obtained. Finally, a Bayesian variable selection approach is applied to a hierarchical logistic regression model and posterior model probabilities for the equality of the correlated proportions are estimated. This latter approach has the feature that the posterior model probabilities depend on the maindiagonal cells.en
heal.journalNameThe Indian Journal of Statisticsen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά). ΜΑΘ

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